Subjective route risk mapping and mitigation

ABSTRACT

A system for determining a subjective risk score may include a vehicle and/or a computing device associated with a user travelling within the vehicle. The computing device may receive input from the user when the user feels a sense of unease regarding a particular road segment upon which the vehicle is traveling. The system may further include a subjective risk analysis computing system that may be communicatively coupled to the computing device. The subjective risk analysis computing system may receive subjective risk information corresponding to the user&#39;s sense of unease regarding particular road segments and may process the subjective risk information to determine a subjective risk score for each of a plurality of road segments along a route. An insurance company may use this information to determine whether to adjust a quote or premium of an insurance policy.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of and is a continuation of U.S.patent application Ser. No. 16/294,103, filed Mar. 6, 2019, and entitled“Subjective Route Risk Mapping and Mitigation,” which is a continuationof and claims priority to U.S. patent application Ser. No. 15/013,523,filed Feb. 2, 2016, and entitled “Subjective Route Risk Mapping andMitigation,” which is related to U.S. patent application Ser. No.14/100,913, filed Dec. 9, 2013, and entitled “Route Risk Mitigation,”which is a continuation of U.S. patent application Ser. No. 12/118,021,filed May 9, 2008, issued Dec. 10, 2013, as U.S. Pat. No. 8,606,512.Each of the foregoing applications is incorporated by reference hereinin its entirety.

TECHNICAL FIELD

Aspects of this disclosure relate generally to risk mitigation. Moreparticularly, aspects of this disclosure relate to using geographicallyencoded information to promote and/or reward risk mitigation.

DESCRIPTION OF THE RELATED ART

Drivers may experience varying levels of ease and unease while driving aroute in a vehicle. Although insurers may vary insurance premiums basedon garaging location (by state, county, etc.), there is a need in theart for enhanced systems and methods to better account for variations ina location-based subjective risk experienced by drivers and subsequentlyacting accordingly. For example, some insurers use location-basedtechnology such as GPS (global positioning satellites) to monitor thelocation of vehicles. Nevertheless, there is a need in the art for atechnique for estimating the subjective risk associated with a routeusing the various aspects disclosed by the present invention. Therefore,there is a benefit in the art for an enhanced method and device forcalculating a subjective risk for a road segment and using it to, amongother things, mitigate risk.

SUMMARY

Aspects of this disclosure overcome problems and limitations of theprior art by providing a method for mitigating the risks associated withdriving by assigning subjective risk values to road segments and usingthose risk values to select less subjectively risky travel routes.

Various approaches to helping users mitigate subjective risks arepresented. In accordance with aspects of this disclosure, a system mayinclude a vehicle, a computing device associated with a user travellingwithin the vehicle and/or a subjective risk analysis computing systemthat may be communicatively coupled to the computing device. In somecases, the computing device may receive input from the user when theuser feels a sense of unease, or other subjective emotion, regarding aparticular road segment, level of traffic, etc. The subjective riskanalysis computing system may receive subjective risk informationcorresponding to the user's sense of unease regarding particular roadsegments and process the subjective risk information to determine asubjective risk score for each of a plurality of road segments along aroute. In some cases, the input device may further include, or becommunicatively coupled to, a pressure sensor (e.g., a pressuretransducer) accessible to the user. The pressure transducer may generatea pressure signal corresponding to a level of unease being experiencedby the user during a subjective risk event. In some cases, thesubjective risk analysis computing system may analyze the pressuresignal when determining the subjective risk score for each of aplurality of road segments along the route. In some cases, thesubjective risk analysis system may be used to analyze a plurality ofsubjective risk scores corresponding to the plurality of road segmentsalong a route. The plurality of subjective risk scores may correspond toaggregated information corresponding to a plurality of drivers that havetraveled the same plurality of road segments and have experienced someform of unease while traveling along the route. In some cases, the inputdevice may include one or more device in addition to, or in place of thepressure sensor, such as a button, a microphone for receiving a verbalinput, a biometric sensor (e.g., a pulse sensor, a heartrate sensor, ablood pressure sensor, etc.), a device for receiving haptic feedback, animaging device (e.g., a video camera, a still camera, etc.) that may beused to monitor eye movements, body language, and the like.

In some cases, a vehicle may include an input device accessible to anoccupant of the vehicle. The input device may receive subjective riskinformation regarding the occupant's emotional reaction (e.g., a senseof ease or unease) regarding a plurality of road segments upon which thevehicle is travelling. In some cases, the input device may comprise apressure transducer, a switch, a microphone, or other form of inputdevice. The vehicle may further include a communication interfacecommunicatively coupled to the input device. The communication interfacemay be used for communicating the subjective risk information via awireless communication protocol to a remote computing system. In somecases, the remote computing system may include a subjective riskanalysis engine that may be used for analyzing the subjective riskinformation received from the user to determine a subjective risk scoreassociated with the user.

The details of these and other embodiments of this disclosure are setforth in the accompanying drawings and description below. Other featuresand advantages of aspects of this disclosure will be apparent from thedescription and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of this disclosure may take physical form in certain parts andsteps, embodiments of which will be described in detail in the followingdescription and illustrated in the accompanying drawings that form apart hereof, wherein:

FIGS. 1A and 1B depict illustrative block diagrams of operatingenvironments in accordance with aspects of this disclosure;

FIG. 2 depicts an illustrative block diagram of a system for generatingand using subjective risk information associated with one or more usersin accordance with aspects of this disclosure;

FIG. 3 depicts illustrative block diagrams of vehicle types that may beutilized by a driver in accordance with aspects of this disclosure;

FIG. 4 depicts an illustrative block diagram of an interior space of avehicle accessible at least to a driver of the vehicle in accordancewith aspects of this disclosure;

FIG. 5 depicts an illustrative method for determining a subjective riskscore associated with a driver of a vehicle in accordance with aspectsof this disclosure;

FIG. 6 depicts an illustrative method for processing subjective riskinformation provided by a user while traveling upon a road segment inaccordance with aspects of this disclosure.

It will be apparent to one skilled in the art after review of theentirety disclosed that the steps illustrated in the figures listedabove may be performed in other than the recited order, and that one ormore steps illustrated in these figures may be optional.

DETAILED DESCRIPTION

Systems and methods in accordance with aspects of this disclosure may beprovided to generate and create a map reflecting how people feel about adriving route. Such a map may be generated using information receivedfrom a user regarding a driver's feelings and/or emotions (e.g., unease,fear, frustration, anger, relaxation, bored, sleepy, confident, alert,aware, confused, etc.) regarding different road segments along theirroute. For example, the driver may provide information corresponding towhat aspects of the route make that particular driver nervous or afraid.The driver may enter information regarding their feelings towards roadconditions (e.g., potholes, standing water, turns, bridges, narrowlanes, darkness, etc.), a time of day, weather conditions (e.g., rain,fog, wind, storms, an angle of sunshine, etc.), environmental hazards(e.g., debris in the road, a threat of debris falling into the roadway,smoke from a nearby fire, etc.), a particular human condition, and/orother people within the vehicle (e.g., a number of people in thevehicle, a noise level within the vehicle, a number of children beingpresent, etc.), traffic flow, one or more traffic patterns, a trafficamount (e.g., heavy traffic), a time of day (e.g., night driving, rushhour, etc.), an event that may have an effect on traffic congestion(e.g., a concert, a sporting event, a political rally, etc.), pedestriantraffic (e.g., e.g., a crosswalk, a school zone, etc.), and the like. Insome cases, the information may be gathered in near real-time, at timeintervals during a trip, before a trip, after a trip, or the like.

When information is gathered during a trip, either in near-real time, orat time intervals, the information may be collected via one or moreinput devices that may include a voice-based application or a buttoneasily (and safely) accessible to the driver or other occupant. Forexample, a driver may speak a key word (e.g., “now”) or press a buttonto indicate a time at which the driver is not at ease. In some cases,the information is entered as a binary entry. For example, the driver isor is not nervous. In other cases, the driver may be able to indicate alevel of unease when providing the information. For example, the drivermay be prompted at the conclusion of the trip to enter additionalinformation about times that indicate a feeling of unease. In othercases, the user input device may be capable of providing the additionalinformation at the time of entry. For example, the user input device maynot only be capable of detecting a time at which the driver indicatesthe feeling of unease, but also sense a level of unease at the time. Forexample, a physical button may include a pressure sensor, where apressure level may be analyzed to determine a level of unease,particularly in comparison with different entries made by the samedriver. In some cases, a light pressure may be determined to indicate alow level of disquiet, thus indicating a mild reaction to a subjectiverisk event experienced on the road. Similarly, higher pressures appliedto the button may be determined to indicate greater feelings of uneaseto the particular subjective risk experienced by the user. In somecases, a computer device may analyze the information to determine, usingone or more mathematical algorithms (e.g., a mathematical algorithmcustomized for each driver, a unique algorithm for a group of drivers,etc.), a level of unease to be associated with each subjective riskexperienced along the route. Further, a duration of time associated withhow long pressure has been applied to the button and/or a duration oftime associated with an amount of applied pressure may be used indetermining the level of unease for the particular occupant of thevehicle. In some cases, one or more sensors may be used (e.g., biometricsensors, pressure sensors, microphones, etc.) to generate a signalrepresentative of a driver's feeling of unease, without the driverconsciously providing the information. For example, one or morebiometric sensors may be used to sense an increase in a heart rate,breathing rate, and/or the like. In other cases, a pressure sensor maybe embedded within the steering wheel of the vehicle and configured forsensing a grip pressure. Such examples illustrative and are not to limitthe sensor type or location to the enumerated examples.

In some cases, the one or more mathematical algorithms may bepersonalized based on information corresponding to a particular driver.For example, the mathematical algorithm may include one or moreweighting variables that may be adjusted based on a particular driver'sprofile, driving history, subjective risk scores and/or the like In anillustrative example, an algorithm may utilize a linear relationshipbetween pressure and the determined level of unease for a particulardriver on a particular road or type of road in determining a value forthe one or more weighting variables.

In some cases, a personalized mathematical algorithm may be generatedfor each driver or group of drivers (e.g., student drivers, driverswithin a specified age range, etc.). For example, an algorithm mayinclude one or more weighting factors that may be adjusted based oncharacteristics of a particular driver. By using personalizedalgorithms, the same road segment may have a different subjective riskscore based on the personalized weighting factors for each driver. In anillustrative example, an algorithm for scoring a particular road segmentmay include one or more weighting factors associated with differentfeatures that may be encountered on the road. For example, the roadsegment may include one or more unprotected left turns, where a firstdriver may be confident in performing such turns, therefore anassociated weighting factor may be used to provide a low weight (e.g.,0.1, 0.2, etc.) to this feature. However, a second person (e.g., aninexperienced driver) may have a greater sense of unease when performingan unprotected left turn, so that the weighting factor may cause thisroad feature to have an increased weight (e.g., 0.6, 0.7, etc.) in thecalculation of the subjective risk score. By customizing orpersonalizing the mathematical algorithm, the resulting subjective riskscore will be a more accurate predictor of how risky a particular roadsegment will be perceived by each driver. For example, the mathematicalalgorithm customized for the first driver may predict that the firstdriver will experience low subjective risk (e.g., a subjective riskscore of 20, 30, etc.), while the mathematical algorithm customized forthe second driver may predict that the second driver will experience ahigher subjective risk along the same road segment. (e.g., a subjectiverisk score of 60, 70, etc.). In some cases, the mathematical algorithmand/or the weighting factors used in the mathematical algorithm may beupdated for the particular driver upon entry of new information in nearreal-time, at a defined interval, upon a driver profile update, when anapplication is started or stopped and/or the like.

In some cases, the subjective risk map, the subjective risk scores, or acombination may be used to generate educational and/or training routesto assist drivers in improving one or more aspects of their driving.While all drivers may benefit from such training routes, student driversand/or newly licensed drivers may benefit the most. For example, thesubjective risk data may be used to determine routes (e.g., a pluralityof route segments) that let the drivers practice types of roads thatthey are just slightly uncomfortable with so that they can improve andbecome comfortable on those roads. Each driver may generate a customroute based on a personalized mathematical algorithm and/or subjectiverisk profile. The driver may then be moved to other types of roads onwhich they need experience. In such cases, the subjective risk mapand/or subjective risk information may be used, not to minimize risk,but rather to generate routes that give a very slight subjective risk,to allow a driver to practice and become more comfortable.

In some cases, when a driver experiences a lack of unease for too long,even in safe driving conditions (e.g., a route segment with a lowobjective risk score), the driver may become more risky because he/shemay lose focus on the road. In such cases, a route may be generatedwhere, rather than reducing subjective risk, a route may be generatedwhere the route segments include a specified level of subjective riskalong the entire route. This route may actually be safer because itallows the driver to become more focused, than those driving a routewith a minimized level of subjective risk. In some cases, a route may begenerated such that the route segments may alternate between periods ofsome subjective risk (e.g., medium subjective risk, high subjectiverisk, etc.) and periods of low or no subjective risk. In some cases,such a route may ultimately result in being the safest route. As such,the customizable algorithms may be used to determine that subjectiverisk may not need to be minimized to maximize safety, and that there mayeven be a target subjective risk level (e.g., 30% subjective risk, 40%subjective risk, etc.) that maximizes safety for each driver. In somecases, the target subjective risk level may be a set target for alldrivers, a group of drivers, or may be customizable for each individualdriver.

In some cases, the one or more algorithms may each utilize a differentrelationship based on an equation or an exponential relationship betweenthe sensed pressure and the determined level of unease. In some cases, apersonalized algorithm may utilize one or more thresholds to indicate alevel of unease, where a pressure below a threshold may indicate alesser level of unease and a pressure above the threshold may indicate ahigher level of unease. In some cases, other parameters may be usedinstead of, or in addition to, pressure, such as a time duration duringwhich the button was depressed, where the length of time may be analyzedto determine a level of unease, with or without an associated pressurereading. In cases where the information entry method uses a microphone,the keyword and/or tone of voice may be analyzed using algorithmssimilar to those discussed above, where a presence of a keyword mayindicate that the driver is experiencing unease, and a tone of voiceand/or length of time the driver speaks may be used to identify anaccompanying level of unease. In many cases, the subjective riskidentified for different road segments may be aggregated over apopulation of drivers (e.g., all drivers, drivers with similarcharacteristics, etc.) to determine a level of risk associated with thedifferent road segments.

A subjective risk analysis system may receive information via a networkfrom one or more drivers and analyze the subjective risk information todetermine a subjective risk score associated with each driver based on amathematical algorithm. In some cases, the subjective risk informationmay be used as inputs to the mathematical algorithm and/or used tomodify the algorithm itself so that the mathematical algorithm may becustomized for each driver. By using such a customizable algorithm, thesubjective risk score may be personalized for individual drivers such asby using personalized weighting factors for different road segments,geographical areas, road times, and the like. This subjective risk scoremay be representative of types and levels of risks that the driver mayexperience upon a route before experiencing a level of unease. Further,the subjective risk score may be used to represent that at least aportion (e.g., a specified percentage) of a route upon which aparticular driver may experience a minimum of subjective risk. Thesubjective risk score may also be used to represent a pattern of riskacross a plurality of route segments of a route, where a transitionbetween a route segment having a low subjective risk and a route segmenthaving a higher subjective risk may be made more gradual to avoid aquick transition between subjective risk levels. In such cases, a routeprovided to a driver may have a subjective risk score for the completeroute may have a higher overall risk than a different route that mayinclude a quick transition between route segments with high subjectiverisk and low subjective risk. This subjective risk score, along with anyidentified risks for which the driver has indicated some level ofunease, may be used to determine a subjective risk map and/or one ormore routes for the driver to follow to minimize an amount of subjectiverisk experienced by the driver during a trip.

In some cases, the subjective risk score may be combined with one ormore other scores related to a risk that may be identified from theroute, such as an objective risk score. The resulting combined riskscore may be output by the computing device as an “overall” risk score.In some cases, the one or more subjective risk scores may be used tosupplement an objective risk score and, vice versa, an objective riskscore may be used to supplement a calculated subjective risk score. Forexample, the subjective risk identified for a route may correspond toone or more road characteristics, such as a case where a person mayexperience a greater level of unease in a road segment having a blindleft-hand turn. An objective risk map having a representation of anobjective risk score for a particular road segment may show anindication that a greater number of accidents may occur along the roadsegment having a blind left-hand turn, however a subjective risk may bereduced for that particular driver as the subjective risk score maypredict that the driver may be more alert when approaching thatparticular road segment.

In some cases, rather than simply minimizing risk, a subjective riskscore may be calculated to provide a desired level of subjective riskfor a route. In an illustrative example, a subjective risk score may becalculated to generate a route having a subjective risk scorecorresponding to an “optimal” level of subjective risk such that thedriver remains alert, but not particularly uncomfortable. In some cases,this optimal level of risk may be customized for each individual driver.For example, the subjective risk scores determined for a particulardriver may be compared to a threshold and/or an average of subjectiverisk scores of similar drivers (e.g., similar in age, experience,demographics, etc.) for different types of road hazards (e.g., anunprotected left turn, road geometry, landscape features, etc.) and/orconditions (e.g., rain, snow, wind, etc.).

For example, this information may be analyzed using one or moremathematical algorithms to determine a location and/or a likelihood thata subjective risk may exist along a route. The subjective risk analysissystem may further incorporate objective risks (e.g., constructionareas, wildlife areas, accident prone areas, dangerous intersections,etc.) when generating the subjective risk map and/or routes. Suchinformation may be overlaid on a map and indicate such subjective risksbased on a particular driver, or a particular grouping of drivers.Drivers may be grouped by any combination of age, relative drivingexperience, a driver license type (e.g., passenger, commercial, etc.), anumber of passengers within the vehicle, a preference of route types(e.g., a fastest route, a route avoiding major roadways, etc.) and/orthe like. In some cases, the drivers may be grouped based on one or moresubjective risk scores that may be stored as a portion of a subjectiverisk profile for each driver. For example, one or more groups of driversmay be formed based on the drivers having similar subjective risk scoresacross a variety of situations that may be encountered along a route orby having similar risk profiles. Such groupings of drivers may be usedto crowd-source the information populating the subjective risk map,where the subjective risks shown on the map may be associated with aparticular driver, a particular class of driver, or other such groupingof similar drivers.

In some cases, a business organization, such as an insurance company,may utilize the information gathered on a subjective risk map, anobjective risk map and the like to determine where the different mapsare aligned or are different. In some cases, the insurance company mayanalyze the information to determine which drivers may experience alower level of unease or a higher level of unease as compared to thetotal population of drivers. In such cases, the business organizationmay use this information to inform business rules and/or policies. Forexample, an insurance company may incorporate such information regardingsubjective risks and/or objective risks into an overall risk score for aparticular driver. In some cases, the overall risk score may be used toidentify educational materials or provide tools that may be used inproviding training for a driver so that the driver may overcome a fear,or otherwise decrease a feeling of unease.

In some cases, in accordance with aspects of this disclosure, one ormore of a personal navigation device, a vehicle, a mobile device, and/ora personal computing device may access a database of risk valuesassociated with a subjective risk map to assist in identifying andpresenting alternate low-risk travel routes. The driver may select amongthe various travel routes presented, taking into account his/hertolerance for risk. In some cases, a particular route may be suggestedas being the route with the lowest associated subjective risk. In somecases, the personal navigation device, the computing system of avehicle, the mobile device and/or the personal computing device may beused to obtain an objective risk score associated with one or moreroutes and/or alternate routes.

FIG. 1A illustrates a block diagram of a computing device (or system)101 (e.g., a subjective risk map generator) in a computer system 100(e.g., a subjective risk mapping system) that may be used according toone or more illustrative embodiments of the disclosure. The computingdevice 101 may have a processor 103 for controlling overall operation ofthe computing device 101 and its associated components, including one ormore memory units (e.g., RAM 105, ROM 107), an input/output module 109,and a memory 115. The computing device 101, along with one or moreadditional devices (e.g., terminals 141 and 151, security andintegration hardware 160) may correspond to any of multiple systems ordevices, such as a subjective risk analysis system and/or an inputdevice configured as described herein for determining a level ofsubjective risk experienced by a driver for a particular road segment,determining an overall level of risk associated with different roadsegments, determining a subjective risk tolerance for a driver, orgrouping of drivers, generating a subjective risk map identifying roadsegments having some level of associated subjective risk, and/orgenerating one or more routes for a driver, or group of drivers based ona desired level of subjective risk exposure. In some cases, the level ofsubjective risk determined for a route may be combined with a level ofobjective risk calculated for the route to determine a combinedobjective/subjective risk score for the route.

The input/output (I/O) 109 may include one or more user interfaces, suchas a microphone, a keypad, one or more buttons, one or more switches, atouch screen, a stylus, one or more pressure sensors, one or morebiometric sensors, and/or one or more other sensors (e.g., anaccelerometer, a gyroscope, etc.) through which a user of the computingdevice 101 may provide input, and may also include one or more of aspeaker for providing audio output and a video display device forproviding textual, audiovisual and/or graphical output. Software may bestored within memory 115 and/or storage to provide instructions toprocessor 103 for enabling device 101 to perform various actions. Forexample, memory 115 may store software used by the device 101, such asan operating system 117, application programs 119, and an associatedinternal database 121. The various hardware memory units in memory 115may include volatile and nonvolatile, removable and non-removable mediaimplemented in any method or technology for storage of information suchas computer readable instructions, data structures, program modules orother data. The memory 115 also may include one or more physicalpersistent memory devices and/or one or more non-persistent memorydevices. The memory 115 may include, but is not limited to, randomaccess memory (RAM) 105, read only memory (ROM) 107, electronicallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, CD-ROM, digital versatile disks (DVD) or otheroptical disk storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium that canbe used to store the desired information and that can be accessed byprocessor 103.

The processor 103 may include a single central processing unit (CPU),which may be a single-core or multi-core processor (e.g., dual-core,quad-core, etc.), or may include multiple CPUs. In some cases, theprocessor 103 may have various bit sizes (e.g., 16-bit, 32-bit, 64-bit,96-bit, 128-bit, etc.) and various processor speeds (ranging from 100MHz to 5 Ghz or faster). The processor 103 and its associated componentsmay allow the system 101 to execute a series of computer-readableinstructions, for example, to determine a subjective risk associatedwith one or more of a plurality of road segments and/or to determine auser's tolerance for subjective risks experienced while traveling alonga route. In some cases, the instructions may be configured to cause theprocessor 103 to determine one or more routes for a user to travelbased, at least in part, on the user's tolerance for subjective risksthat may occur along the route. In other cases, the instructions may beconfigured to cause the processor 103 to determine, using aggregatedsubjective risk information obtained from a plurality of users, acrowd-sourced subjective risk map, where subjective risks may beoverlaid over the map to show areas where drivers experience uneasewhile travelling along a route.

The computing device 101 (e.g., a customer terminal, an insuranceprovider computer hardware memory and processor system, an insuranceclearinghouse computer memory and processor device, etc.) may operate ina networked environment 100 supporting connections to one or more remotecomputers, such as terminals 141 and 151. The terminals 141 and 151 maybe personal computers, servers (e.g., web servers, database servers), ormobile communication devices (e.g., mobile phones, portable computingdevices, vehicles, and the like), and may include some or all of theelements described above with respect to the computing device 101. Insome cases, the terminals 141, 151 may be located at one or moredifferent geographic locations, including, but not limited to, at acustomer location, a site associated with an insurance agent and/oragency and/or a site associated with an insurance provider. The networkconnections depicted in FIG. 1A include a local area network (LAN) 125and a wide area network (WAN) 129, and a wireless telecommunicationsnetwork 133, but may also include other networks. When used in a LANnetworking environment, the computing device 101 may be connected to theLAN 125 through a network interface or adapter 123. When used in a WANnetworking environment, the device 101 may include a modem 127 or othermeans for establishing communications over the WAN 129, such as network131 (e.g., the Internet, a cellular network, and the like). When used ina wireless telecommunications network 133, the device 101 may includeone or more transceivers, digital signal processors, and additionalcircuitry and software for communicating with wireless computing devices141 (e.g., mobile phones, portable customer computing devices) via oneor more network devices 135 (e.g., base transceiver stations) in thewireless network 133.

Also illustrated in FIG. 1A is a security and integration layer 160,through which communications may be sent and managed between thecomputing device 101 and the remote devices (141 and 151) and remotenetworks (125, 129, and 133). The security and integration layer 160 maycomprise one or more computing devices, such as web servers,authentication servers, and various networking components (e.g.,firewalls, routers, gateways, load balancers, etc.), having some or allof the elements described above with respect to the computing device101. As an example, security and integration layer 160 may comprise aset of web application servers configured to use secure protocols and toinsulate the computing device 101 (e.g., one or more servers, aworkstation, etc.) from external devices 141 and 151. In some cases, thesecurity and integration layer 160 may correspond to a set of dedicatedhardware and/or software operating at the same physical location andunder the control of same entities as the computing device 101. Forexample, the layer 160 may correspond to one or more dedicated webservers and network hardware in a data center or in a cloudinfrastructure supporting a cloud-based application and/or process. Inother examples, the security and integration layer 160 may correspond toseparate hardware and software components which may be operated at aseparate physical location and/or by a separate entity.

In some cases, the data transferred to and from computing device 101 inmay include secure and sensitive data, such as historical vehiclelocation information, real-time vehicle location and/or statusinformation, insurance customer and policy data, etc. Therefore, it maybe desirable to protect the data transmission by using secure networkprotocols and encryption, and also to protect the integrity of the datastored when on the computing device 101 using the security andintegration layer 160 to authenticate users and restrict access tounknown or unauthorized users. In various implementations, security andintegration layer 160 may provide, for example, a file-based integrationscheme or a service-based integration scheme. In file-based integration,data files may be transmitted to and from the computing device 101through the security and integration layer 160, using various networkcommunication protocols. Secure data transmission protocols and/orencryption may be used in file transfers to protect to integrity of thedata, for example, File Transfer Protocol (FTP), Secure File TransferProtocol (SFTP), and/or Pretty Good Privacy (PGP) encryption.

In service-based integration, one or more web services may beimplemented within the system 100 between the computing device 101and/or security and integration layer 160. The web services may beaccessed by authorized external devices and users to support input,extraction, and manipulation of the data in the computing device 101.Web services built to support the system 100 may be cross-domain and/orcross-platform, and may be built for enterprise use. Such web servicesmay be developed in accordance with various web service standards, suchas the Web Service Interoperability (WS-I) guidelines. In some examples,system web service may be implemented in the security and integrationlayer 160 using the Secure Sockets Layer (SSL) or Transport LayerSecurity (TLS) protocol to provide secure connections between thecomputing device 101 and various clients 141 and 151 attempting toaccess, insert and/or manipulate data within the system 100. SSL or TLSmay use HTTP or HTTPS to provide authentication and/or confidentiality.In some cases, system web service may be implemented using theWS-Security standard, which provides for secure SOAP messages using XMLencryption. In still other examples, the security and integration layer160 may include specialized hardware for providing secure web services.For example, secure network appliances in the security and integrationlayer 160 may include built-in features such as hardware-accelerated SSLand HTTPS, WS-Security, and firewalls. Such specialized hardware may beinstalled and configured in the security and integration layer 160 infront of the web servers, so that any external devices may communicatedirectly with the specialized hardware.

Although not shown in FIG. 1A, various elements within the memory 115 orother components in the system 100, may include one or more caches, forexample, CPU caches used by the processing unit 103, page caches used bythe operating system 117, disk caches of a hard drive, and/or databasecaches used to cache content from database 121. For embodimentsincluding a CPU cache, the CPU cache may be used by one or moreprocessors in the processing unit 103 to reduce memory latency andaccess time. In such examples, a processor 103 may retrieve data from orwrite data to the CPU cache rather than reading/writing to memory 115,which may improve the speed of these operations. In some examples, adatabase cache may be created in which certain data from a database 121may be cached in one or more separate smaller databases on anapplication server separate from the database server. For instance, in amulti-tiered application, a database cache on an application server canreduce data retrieval and data manipulation time by not needing tocommunicate over a network with a back-end database server. These typesof caches and others may be included in various embodiments, and mayprovide potential advantages in certain implementations of the system100.

It will be appreciated that the network connections shown areillustrative and other means of establishing a communications linkbetween the computers may be used. The existence of any of variousnetwork protocols such as TCP/IP, Ethernet, FTP, HTTP and the like, andof various wireless communication technologies such as GSM, CDMA, Wi-Fi,Bluetooth, WiMAX, etc., is presumed, and the various computer devicesand insurance clearinghouse system components described herein may beconfigured to communicate using any of these network protocols ortechnologies.

Additionally, one or more application programs 119, such as a subjectiverisk map determination application, may be used by one or more computingdevices (e.g., the computing device 101) within the system 100,including computer executable instructions for identifying a subjectiverisk tolerance for a driver (or owner, passenger, parent of the driver,etc.) of a vehicle, identifying one or more road segments upon which thedriver experiences some level of subjective risk, generating asubjective risk score associated with the driver corresponding to atolerance level towards subjective risks, generating a subjective riskmap based on aggregated subjective risk information received from aplurality of drivers, determining a plurality of routes for suggestionto a driver based on the risk tolerance of the driver or group ofsimilar drivers.

Referring to FIG. 1B, an example of a suitable operating environment inwhich various aspects of this disclosure may be implemented is shown inthe architectural diagram of FIG. 1B. The operating environment is onlyone example of a suitable operating environment and is not intended tosuggest any limitation as to the scope of use or functionality of thisdisclosure. The operating environment may be comprised of one or moredata sources 104 b, 106 b in communication with a computing device 102b. The computing device 102 b may use information communicated from thedata sources 104 b, 106 b to generate values that may be stored in aconventional database format. In one embodiment, the computing device102 b may be a high-end server computer with one or more processors 114b and memory 116 b for storing and maintaining the values generated. Thememory 116 b storing and maintaining the values generated need not bephysically located in the computing device 102 b. Rather, the memory(e.g., ROM, flash memory, hard drive memory, RAID memory, etc.) may belocated in a remote data store (e.g., memory storage area) physicallylocated outside the computing device 102 b, but in communication withthe computing device 102 b.

A personal computing device 108 b (e.g., a personal computer, tablet PC,handheld computing device, personal digital assistant, mobile device,etc.) may communicate with the computing device 102 b. Similarly, apersonal navigation device 110 b (e.g., a global positioning system(GPS), geographic information system (GIS), satellite navigation system,mobile device, other location tracking device, etc.) may communicatewith the computing device 102 b. The communication between the computingdevice 102 b and the other devices 108 b, 110 b may be through wired orwireless communication networks and/or direct links. One or morenetworks may be in the form of a local area network (LAN) that has oneor more of the well-known LAN topologies and may use a variety ofdifferent protocols, such as Ethernet. One or more of the networks maybe in the form of a wide area network (WAN), such as the Internet. Thecomputing device 102 b and other devices (e.g., devices 108 b, 110 b)may be connected to one or more of the networks via twisted pair wires,coaxial cable, fiber optics, radio waves or other media. The term“network” as used herein and depicted in the drawings should be broadlyinterpreted to include not only systems in which devices and/or datasources are coupled together via one or more communication paths, butalso stand-alone devices that may be coupled, from time to time, to suchsystems that have storage capability.

In another embodiment in accordance with aspects of this disclosure, apersonal navigation device 110 b may operate in a stand-alone manner bylocally storing some of the database of values stored in the memory 116b of the computing device 102 b. For example, a personal navigationdevice 110 b (e.g., a GPS in an automobile) may be comprised of aprocessor, memory, and/or input devices 118 b and/or output devices 120b (e.g., keypad, display screen, speaker, etc.). The memory may becomprised of a non-volatile memory that stores a database of values usedin calculating an estimated route risk for identified routes. Therefore,the personal navigation device 110 b need not communicate with acomputing device 102 b located at, for example, a remote location inorder to calculate identified routes. Rather, the personal navigationdevice 110 b may behave in a stand-alone manner and use its processor tocalculate route risk values (e.g., subjective risk values and/orobjective risk values) of identified routes. If desired, the personalnavigation device 110 b may be updated with an updated database ofvalues after a period of time (e.g., an annual patch with new riskvalues determined over the prior year).

In yet another embodiment in accordance with aspects of this disclosure,a personal computing device 108 b may operate in a stand-alone manner bylocally storing some of the database of values stored in the memory 116b of the computing device 102 b. For example, a personal computingdevice 108 b may be comprised of a processor, memory, input device(e.g., keypad, CD-ROM drive, DVD drive, etc.), and output device (e.g.,display screen, printer, speaker, etc.). The memory may be comprised ofCD-ROM media that stores values used in calculating an estimated routerisk for an identified route. Therefore, the personal computing device108 b may use the input device to read the contents of the CD-ROM mediain order to calculate a value for the identified route. Rather, thepersonal computing device 108 b may behave in a stand-alone manner anduse its processor to calculate a route risk value. If desired, thepersonal computing device 108 b may be provided with an updated databaseof values (e.g., in the form of updated CD-ROM media) after a period oftime. One skilled in the art will appreciate that personal computingdevice 108 b, 110 b, 112 b need not be personal to a single user;rather, they may be shared among members of a family, company, etc.

The data sources 104 b, 106 b may provide information to the computingdevice 102 b. In one embodiment in accordance with aspects of thisdisclosure, a data source may be a computer which contains memorystoring data and is configured to provide information to the computingdevice 102 b. Some examples of providers of data sources in accordancewith aspects of this disclosure include, but are not limited to,insurance companies, third-party insurance data providers, governmententities, state highway patrol departments, local law enforcementagencies, state departments of transportation, federal transportationagencies, traffic information services, road hazard information sources,construction information sources, weather information services,geographic information services, vehicle manufacturers, vehicle safetyorganizations, and environmental information services. For privacyprotection reasons, in some embodiments of this disclosure, access tothe information in the data sources 104 b, 106 b may be restricted toonly authorized computing devices 102 b and for only permissiblepurposes. For example, access to the data sources 104 b, 106 b may berestricted to only those persons/entities that have signed an agreement(e.g., an electronic agreement) acknowledging their responsibilitieswith regard to the use and security to be accorded this information.

The computing device 102 b uses the information from the data sources104 b, 106 b to generate values that may be used to calculate anestimated route risk. Some examples of the information that the datasources 104 b, 106 b may provide to the computing device 102 b include,but are not limited to, accident information, geographic information,and other types of information useful in generating a database of valuesfor calculating an estimated route risk. For example, a driver may haveknowledge that accidents may be more common along a particular stretchof a roadway or type of road segment and may experience an increasedlevel of unease as they travel, or approach, a particular road segment.

Some examples of accident information include, but are not limited to,loss type, applicable insurance coverage(s) (e.g., bodily injury,property damage, medical/personal injury protection, collision,comprehensive, rental reimbursement, towing), loss cost, number ofdistinct accidents for the segment, time relevancy validation, cause ofloss (e.g., turned left into oncoming traffic, ran through red light,rear-ended while attempting to stop, rear-ended while changing lanes,sideswiped during normal driving, sideswiped while changing lanes,accident caused by tire failure (e.g., blow-out), accident caused byother malfunction of car, rolled over, caught on fire or exploded,immersed into a body of water or liquid, unknown, etc.), impact type(e.g., collision with another automobile, collision with cyclist,collision with pedestrian, collision with animal, collision with parkedcar, etc.), drugs or alcohol involved, pedestrian involved, wildlifeinvolved, type of wildlife involved, speed of vehicle at time ofincident, direction the vehicle is traveling immediately before theincident occurred, date of incident, time of day, night/day indicator(i.e., whether it was night or day at the time of the incident),temperature at time of incident, weather conditions at time of incident(e.g., sunny, downpour rain, light rain, snow, fog, ice, sleet, hail,wind, hurricane, etc.), road conditions at time of incident (e.g., wetpavement, dry pavement, etc.), and location (e.g., geographiccoordinates, closest address, zip code, etc.) of vehicle at time ofincident.

Accident information associated with vehicle accidents may be stored ina database format and may be compiled per segment. One skilled in theart will understand that the term segment may be interchangeably used todescribe a road segment, intersection, round about, bridge, tunnel,ramp, parking lot, railroad crossing, or other feature that a vehiclemay encounter along a route.

Time relevancy validation relates to the relevancy of historicalaccident information associated with a particular location. Timerelevancy validation information may be dynamically created by comparingthe time frames of accident information to the current date. Forexample, if a location or route had many collisions prior to five yearsago but few since, perhaps a road improvement reduced the risk (such asadding a traffic light). Time relevancy information may be generatedremotely and transmitted by a data source 104, 106 to the computingdevice 102 like other information. Alternatively, time relevancyinformation may be calculated at the computing device 102 using otherinformation transmitted by a data source 104, 106. In some cases, timerelevancy information may be calculated at the computing device withoutreference to data communicated from the data source 104, 106. Forexample, the appropriateness of historical information may be related tothe time frame into which the information belongs. Examples of timeframes may include, but are not limited to, less than 1 year ago, 1 yearago, 2 years ago, 3 years ago, 4 years ago, 5 to 10 years ago, andgreater than 10 years ago. In one embodiment, the more recent thehistorical information, the greater weight is attributed to theinformation.

Some examples of geographic information include, but are not limited to,location information and attribute information. Examples of attributeinformation include, but are not limited to, information aboutcharacteristics of a corresponding location described by some locationinformation: posted speed limit, construction area indicator (i.e.,whether location has construction), topography type (e.g., flat, rollinghills, steep hills, etc.), road type (e.g., residential, interstate,4-lane separated highway, city street, country road, parking lot, etc.),road feature (e.g., intersection, gentle curve, blind curve, bridge,tunnel), number of intersections, whether a roundabout is present,number of railroad crossings, whether a passing zone is present, whethera merge is present, number of lanes, width of road/lanes, populationdensity, condition of road (e.g., new, worn, severely damaged withsink-holes, severely damaged with erosion, gravel, dirt, paved, etc.),wildlife area, state, county, and/or municipality. Geographicinformation may also include other attribute information about roadsegments, intersections, bridges, tunnels, railroad crossings, and otherroadway features.

Location information for an intersection may include the latitude andlongitude (e.g., geographic coordinates) of the geometric center of theintersection. The location may be described in other embodiments using aclosest address to the actual desired location or intersection. Theintersection (i.e., location information) may also include informationthat describes the geographic boundaries, for example, of theintersection which includes all information that is associated within acircular area defined by the coordinates of the center of theintersection and points within a specified radius of the center. Inanother example of location information, a road segment may be definedby the latitude and longitude of its endpoints and/or an area defined bythe road shape and a predetermined offset that forms a polygon. Segmentsmay comprise intersections, bridges, tunnels, rail road crossings orother roadway types and features. Those skilled in the art willrecognize that segments can be defined in many ways without departingfrom the spirit of this disclosed invention.

Some examples of vehicle information include, but are not limited to,information that describes vehicles that are associated with incidents(e.g., vehicle accidents, etc.) at a particular location (e.g., alocation corresponding to location information describing a segment,intersection, etc.) Vehicle information may include vehicle make,vehicle model, vehicle year, and age. Vehicle information may alsoinclude information collected through one or more in-vehicle devices orsystems such as an event data recorder (EDR), onboard diagnostic system,or global positioning satellite (GPS) device; examples of thisinformation include speed at impact, brakes applied, throttle position,direction at impact. As is clear from the preceding examples, vehicleinformation may also include information about the driver of a vehiclebeing driven at the time of an incident. Other examples of driverinformation may include age, gender, marital status, occupation, alcohollevel in blood, credit score, distance from home, cell phone usage(i.e., whether the driver was using a cell phone at the time of theincident), number of occupants.

In one embodiment in accordance with aspects of this disclosure, a datasource 104 b may provide the computing device 102 b with accidentinformation that is used to generate values (e.g., create new valuesand/or update existing values). The computing device 102 b may use atleast part of the received accident information to calculate a value,associate the value with a road segment (or other location information),and store the value in a database format. One skilled in the art willappreciate, after thorough review of the entirety disclosed herein, thatthere may be other types of information that may be useful in generatinga database of values for use in, among other things, calculating anestimated route risk.

For example, in accordance with aspects of this disclosure, a datasource 104 b may provide the computing device 102 b with geographicinformation that is used to generate new roadway feature risk values ina database of risk values and/or update existing risk values; where theroadway feature may comprise intersections, road segments, tunnels,bridges, or railroad crossings. Attributes associated with roadways mayalso be used in part to generate risk values. The computing device 102 bmay use at least part of the received geographic information tocalculate a value, associate the value with a road segment (or otherlocation information), and store the value in a database format.Numerous examples of geographic information were provided above. Forexample, a computing device 102 b may receive geographic informationcorresponding to a road segment comprising accident information androadway feature information and then calculate a risk value. Therefore,when calculating a risk value, the system may use, in one example, thegeographic information and the accident information (if any accidentinformation is provided). In alternative embodiments in accordance withaspects of this disclosure, the computing device may use accidentinformation, geographic information, vehicle information, and/or otherinformation, either alone or in combination, in calculating risk valuesin a database format.

The values generated by the computing device 102 b may be associatedwith a road segment containing the accident location and stored in adata store. Similar to a point of interest (POI) stored in GPS systems,a point of risk (POR) is a road segment or point on a map that has riskinformation (e.g., subjective risk, objective risk, etc.) associatedwith it. Points of risk may arise because incidents (e.g., accidents)have occurred at these points before. In accordance with aspects of thisdisclosure, the road segment may be a predetermined length (e.g., ¼mile) on a stretch of road. Alternatively, road segments may be points(i.e., where the predetermined length is minimal) on a road.Furthermore, in some embodiments, road segment may include one or moredifferent roads that are no farther than a predetermined radius from aroad segment identifier. Such an embodiment may be beneficial in alocation, for example, where an unusually large number of streetsintersect, and it may be impractical to designate a single road for aroad segment.

FIG. 2 depicts an illustrative block diagram of a system 200 forgenerating and using subjective risk information, such as by generatingone or more subjective risk maps, associated with one or more users inaccordance with aspects of this disclosure. The system may include avehicle 210, one or more user devices 220 associated with a user (e.g.,a driver, a passenger, etc.) of the vehicle, and a remote computingsystem 240 that may be associated with a business entity (e.g., aninsurance provider, a vehicle manufacturer, a global positioningcompany, etc.) or governmental agency having an interest in assessingand/or minimizing a subjective risk associated with one or more segmentsof road upon which the user travels within the vehicle. The one or moreuser devices 220 may include a variety of personal computing devicesincluding, but not limited to, a phone (e.g., a smart phone 220 a), apersonal computer 220 b, a laptop computer 220 c, a tablet computer 220d, a personal navigation device 110 b, a vehicle's computer system,and/or the like. In some cases, the user devices 220 may comprise theillustrative user device 230 that may include a user interface 231 thatmay be capable of displaying one or more user interface screens 233 on adisplay device 235. The user interface screens 233 may include screensfor displaying information to the user and/or receiving information fromthe user. The user device 230 may further include a processor 237, oneor more memory devices 239 and a communication interface 238. In somecases, one or more of the user interface 231, the user interface screens233, the display device 235, the processor 237, the one or more memorydevices 239, and/or the communication interface 238 may be implementedsimilarly to the corresponding features discussed in reference to FIGS.1A and 1B.

In some cases, one or more devices associated with the user and/orvehicle 210 may communicate via one or more wired or wireless networks205 to the remote computing system 240. For example, the remotecomputing system 240 may include one or more of a subjective riskanalysis system 250, an objective risk analysis system 260 and/or aninsurance quotation system 290. In an illustrative example, thesubjective risk analysis system 250 may be configured to generate asubjective risk profile (e.g., a subjective risk class, a subjectiverisk score, etc.) for one or more users, such as insurance customers ofan insurance company. The subjective risk analysis system 250 mayutilize demographic information associated with the user, along withsubjective risk information solicited or otherwise received from theuser when generating the subjective risk profile. In some cases, thesubjective risk analysis system 250 may include a subjective riskanalysis engine 252 configured to generate subjective risk profilesand/or subjective risk maps associated with customers of the businessorganization. The subjective risk analysis engine 252 may utilizeinformation received from one or more remote devices associated with theuser (e.g., the vehicle 210, the user interface device 212, the inputdevice 214, and the user devices 220, 230) via the one or more networks.In some cases, the subjective risk analysis system 250 may be used togenerate and/or store one or more subjective risk maps associated with agroup of users (e.g., an age group, a driving experience group, etc.).

In some cases, the objective risk analysis system 260 may be usedidentify and/or mitigate one or more objective risks associated with aroute that may be traveled by the vehicle 210. For example, theobjective risk analysis system 260 may be configured to usegeographically encoded information to reward and/or promote riskmitigation of identified objective risks (e.g., an accident, a dangerousroad segment, etc.) as discussed in co-pending U.S. patent applicationSer. No. 14/100,913, filed Dec. 9, 2013, and entitled “Route RiskMitigation,” which is a continuation of U.S. patent application Ser. No.12/118,021, filed May 9, 2008, issued Dec. 10, 2013 as U.S. Pat. No.8,606,512, which claims priority to U.S. Provisional Patent ApplicationNo. 60/917,169 filed May 10, 2007. All of the aforementioned areincorporated by reference in their entirety herein.

One or more of the objective risk analysis system 260 and/or thesubjective risk analysis system 250 may access, via the wired orwireless networks 205, information provided by one or more computersystems 281 associated with a plurality of third-party informationsources 280. Illustrative examples of third-party data sources mayinclude the one or more data sources 104 b, 106 b discussed above.Further, one or more devices within the insurance quotation system 290,the third party information sources 280, the mapping information, theobjective risk analysis system 260, and/or the subjective risk analysissystem 260 may be implemented using computing devices (e.g., thecomputing device 101, 102 a, etc.) discussed in reference to FIGS. 1Aand 1B.

The remote computing system 240 may further include one or moredatabases for storing mapping information 270. In some cases, themapping information may include geocoded mapping information storedwithin a map database 272. The geocoded mapping information may include,but not be limited to the location and mapping information discussedabove, such as information about characteristics of a correspondinglocation (e.g., posted speed limit, construction area indicator,topography type, road type, road feature, number of intersections,whether a roundabout is present, number of railroad crossings, whether apassing zone is present, whether a merge is present, number of lanes,width of road/lanes, population density, condition of road, wildlifearea, state, county, and/or municipality). The mapping information 270may also include other attribute information about road segments,intersections, bridges, tunnels, railroad crossings, and other roadwayfeatures. The mapping information 270 may further include the addressand/or latitude and longitude of noted geographic features and/or thecharacteristics of the corresponding location.

In an illustrative example, the vehicle 210 may include a user interface212 and an input device 214 accessible to an occupant of the vehicle.The user interface 212 may include a display and/or speakers forpresenting audio and/or video information to the user. For example, thedisplay may be used to present geographic information (e.g., a map, aroute, etc.) and/or vehicle information (e.g., temperature information,vehicle speed, tire pressure, radio information, cabin environmentalinformation, etc.) to the user via one or more user interface screens.In some cases, the user interface 212 may present the geographicinformation and/or the vehicle information as an audio message presentedusing speakers installed within the vehicle. In some cases, the userinterface 212 may include a personal computing device 108 b, such as thepersonal navigation device 110 b, a smart phone 220 a, a laptop computer220 c, and/or the tablet computer 220 d. The personal navigation device110 b may be located, for example, in the vehicle 210 or in a mobiledevice (e.g., the smart phone 220 a, the tablet computer 220 d, etc.)with location tracking capabilities. In some cases, the input device 214may include one or more means for the driver and/or passenger in thevehicle to provide information about how they feel regarding a pluralityof road segments or type of road segment (e.g., subjective riskinformation). The input device may be configured to provide thesubjective risk information in real-time or near real-time to the remotecomputing system 240 for processing.

In some cases, the driver and/or passenger in the vehicle 210 mayprovide subjective risk information (e.g., using the user interface 212,the input device 214 and/or the user devices 220) regarding how theyfeel towards a particular road segment or type of road segment (e.g., asense of unease, anxiety, fear, etc.) to be processed by the subjectiverisk analysis engine 252 using one or more algorithms to determine oneor both of a subjective risk score associated and/or a subjective riskclass associated with the user. For example, a user may experience somesense of unease based on a witnessed accident, or knowledge of aprevious accident along the route, or in a known location. In somecases, the way a person drives and/or acts in a particular drivingsituation may be influenced by the perceived risk (e.g., a subjectiverisk), along with any objective risk, present during a time the vehicleis on a particular road segment. In some cases, the subjective riskanalysis engine 252 may use the subjective risk information, along withthe subjective risk score and/or the subjective risk class, to generatea map identifying subjective risks that may be appreciated by thedriver. In some cases, the subjective risk analysis engine 252 may usethe subjective risk information, along with the subjective risk scoreand/or the subjective risk class, to generate a route for use by the usein navigating to a desired location. The identified route may identifyone or more identified subjective risks and/or objective risks that maybe appreciated by the driver and/or may be configured to avoid one ormore of the identified subjective risks and/or the objective risks.

The occupant of the vehicle (e.g., the driver, a passenger, etc.) mayprovide personal preference information corresponding to an exposure tohazards, or other driving situations, that may be experienced whiledriving the vehicle along a route. For example, the driver may bepresented a questionnaire, either on paper, or electronically via one ormore of the personal computing devices 220. The questionnaire may beused to identify a level of risk tolerance towards subjective risksexperienced while driving. For example, the questionnaire may prompt theuser to identify a preferred driving route, such as whether the driverprefers to take the fastest route, one that avoids major roadways, onethat avoids traffic backups, one that avoids unprotected left turns,and/or the like. For example, the questionnaire may prompt the user togive a relative weight (e.g., a ranking, etc.) to one or more identifiedsources of subjective risks to identify a preferred “trade-off” betweena delay time (e.g., minutes spent doing additional driving) versus thedriver's perceived comfort or perceived safety along the route (e.g.,avoiding unprotected left turns, avoiding bridges, avoiding high-trafficroadways, etc.). The answers provided by the driver may be used ingenerating the subjective risk score for the particular driver accordingto one or more customizable mathematical algorithms, such as analgorithm based on weightings associated with each of the answerchoices.

In some cases, the people may answer questions regarding differentdriving situations differently than when the driver experiences similarsituations while driving a vehicle. In such cases, the informationgathered via the questionnaire may be supplemented, or replaced, bysubjective risk information gathered while driving the vehicle.

In some cases, the subjective risk analysis engine 252 may analyze thesubjective risk information entered or otherwise obtained about aparticular driver. Using this subjective risk information, a subjectiverisk profile may be built for each driver where the subjective riskprofile includes personalized subjective risk information that may beused to generate a subjective risk score for one or more road segmentsor types of road segments. The subjective risk profile may includeinformation to generate weighting factors or other such information tocustomize a mathematical algorithm for use in generating a personalizedsubjective risk score associated with each of a plurality of roadsegments for a particular driver. For example, a total subjective risk(R_(ST)) may be customized for the particular road segments comprisingthe route and/or the individual traveling the route. For example, asubjective risk value may be calculated for a route as a sum of thesubjective risk values for the route segments that comprise the totalroute. For example, the subjective risk value of a route may becalculated using a customizable equation, such as the illustrativeequation:

R _(ST) =R _(S1) +R _(S2)+ . . . ,

Further, each route segment may be calculated as a weighted combinationof subjective risks that may be encountered as part of the particularroute segment and may be customized using weighting factors (e.g.,coefficient) customized for each driver. For example, a subjective riskvalue for a route segment may be calculated using the illustrativeequation:

R _(s) =R ₁ +AR _(H) +BR _(ULT) +CR _(W)+ . . . , where

-   -   R_(s): A total subjective risk score for a route segment, which        may be normalized to a defined range of values (e.g., 0=no        subjective risk and 100=Maximum subjective risk)    -   R_(I): A subjective risk contribution associated with this        particular person's general risk based on the subjective inputs        received (e.g., from the pressure sensor).    -   R_(H): A subjective risk contribution associated with hills        within the particular route segment    -   A: A subjective risk coefficient customized for a particular        driver related to driving hilly route segments. May be used with        related route segments (e.g., mountains, etc.)    -   R_(ULT): A subjective risk contribution associated with a number        of unprotected left turns within a particular route segment.    -   B: A subjective risk coefficient customized for a particular        driver related to exposure to unprotected left turns.    -   R_(w): A subjective risk contribution associated with        weather-related issues corresponding to the particular road        segment.    -   C: A subjective risk coefficient customized for a particular        driver related to driving in different weather conditions, such        as rain, snow, wind, clear, etc.

In some cases, the subjective risk analysis engine 252 may use thesubjective risk profile with or without information from the objectiverisk analysis system 260 to generate a subjective risk score for one ormore road segments on which the driver has driven. In some cases, thesubjective risk analysis engine 252 may analyze the subjective riskprofile with or without objective risk information obtained from theobjective risk analysis system 260 to predict a subjective risk scorefor one or more road segments on which the driver has not driven. Forexample, if a driver's subjective risk profile indicates that the drivermay have problems (e.g., feel uneasy) for a particular road segment type(e.g., hilly roads, unprotected left turns, bridges, 5-wayintersections, and the like), the subjective risk analysis engine mayprocess information about a new road segment, such as objective riskinformation, so that the subjective risk analysis engine 252 may predicta subjective risk score that is personalized for each driver, whether ornot the driver has traveled on that route segment. Additionally, thesubjective risk analysis engine 252 may use the subjective risk profileto predict subjective risk scores for a type of road segment as comparedto road segments that are similar, better, or worse than road segmentsalready driven by the driver. For example, a driver may feel uneasydriving in hilly terrain and may have this unease reflected in theirpersonalized subjective risk profile. The subjective risk analysisengine 252 may then process the subjective risk profile to predict asubjective risk score for a route segment for a route to be traveled bythe driver. As such, the subjective risk analysis engine 252 may processa mathematical algorithm that has been customized using the informationof the subjective risk profile to predict a personalized subjective riskscore for a route that the driver has not travelled. Such route segmentsmay be used in generating a route meeting a defined level of subjectiverisk the driver may be exposed over the whole route.

In some cases, the relationship of the driver to the owner of the carmay be factored into the subjective risk calculations. For example, inmany cases, drivers who are driving a car belonging to a family member(e.g., parent, relative, child, sibling, etc.), or an employer, may havea lower tolerance to subjective risks for fear of causing damage to thevehicle. In other cases, such as when a driver is driving a rental car,or a work vehicle, some drivers may have more tolerance towardssubjective risks than when they are driving their own vehicles. As such,in some cases, the subjective risk analysis system 250 may identifyrelationships between drivers, groups of drivers, and vehicles todetermine when, and if, a driver's subjective risk score may beadjusted. For example, the subjective risk analysis system may prompt auser to identify which vehicle will be driven when asked to generate aroute for a trip.

In some cases, a subjective risk map may be generated to include aperson's attitude towards parking, or being near a particular geographiclocation. Such a map may be generated using a combination of subjectiverisk information gathered from a subjective risk profile and/or from anobjective risk data store. Such a map may be used for risk managementover a generated route and/or for parking. Information from parking maybe obtained by low-level monitoring of a location near parking lotsand/or spaces.

FIG. 3 depicts illustrative block diagrams of vehicle types that may beutilized by a driver in accordance with aspects of this disclosure. Forexample, the vehicle types may include a personal vehicle type 310,governmental vehicles, and/or one or more commercial vehicle types320-340, or other vehicle types that may be subject to insurance. Thepersonal vehicle types 310 may include a personal vehicle registered toan individual (or an estate) including, but not limited to a car, aminivan, a van, a truck, a pickup truck, a sports utility vehicle, arecreational vehicle, a motorcycle etc. Illustrative commercial vehicletypes may include fleet vehicles 320 such as taxis, limousines, personalvehicles used for business purposes (e.g., a ride sharing business, adelivery service, a courier service, etc.). Other illustrativecommercial vehicles may include trucks 330 (e.g., a concrete transporttruck, a mobile crane, a dump truck, a garbage truck, a log carrier, arefrigerator truck, a tractor unit, a platform truck, a vehicletransport truck, a flatbed truck, a box truck, a panel van, a tow truck,a canopy express, a pickup truck, a cab-forward truck, a panel truck, apanel van, an ambulance, etc.) and/or buses 340 (e.g., a motor coach, aschool bus, etc.). Other vehicles used for commercial purposes may alsoexist and be applicable to aspects of the disclosure. In some cases, thevehicle types may include other vehicle types that may or may not beincluded in the above vehicle types, such as certain governmentalvehicles (e.g., certain police vehicles, fire trucks, ambulances,military vehicles, etc.), farming equipment (e.g., tractors, combines,harvesters, etc.), recreational vehicles (e.g., boats, off-roadvehicles, etc.), and the like.

FIG. 4 depicts an illustrative block diagram of an interior space of avehicle 400 accessible at least to a driver of the vehicle in accordancewith aspects of this disclosure. Within the interior space of thevehicle, the driver, or other occupant, may have access to one or moreof a steering wheel 410, a user interface device 420 associated with thevehicle, one or more input devices 430 (e.g., a button, a knob, a touchscreen interface, etc.) associated with the user interface device 420, apersonal computing device 440 (e.g., the smart phone 220 a, tablet 220c, laptop 220 c, the personal navigation device 110 b, etc.). In somecases, the personal computing device 440 may be placed in a locationaccessible to the driver. The personal computing device 440 may bephysically mounted or otherwise secured to a surface within interiorspace of the vehicle. In some cases, the personal computing device 440may not be secured to the interior of the vehicle, but located on asurface or within a cavity provided on an interior surface of thevehicle. For example, the personal computing device 440 may be placed ona table or console, or placed within a cup holder or the like. Thepersonal computing device 440 may include one or more input devices(e.g., a physical button, a button implemented on a touch screen display445, a microphone 447, or the like. In some cases, an affixed inputdevice 450 may be provided and accessible to a driver or other occupantof the vehicle 210. For example, the input device may comprise a buttonaffixed to the steering wheel or other surface readily (and safely)accessible to the driver while in motion. In other cases, the inputdevice 450 may comprise a microphone capable of receiving an audio inputfrom the user that may be indicative of a subjective risk event. Theinput device 450 may also comprise one or more sensors capable ofproducing a signal representative of the driver's comfort level (e.g.,level of unease) while driving. Such input devices may include pressuresensors (e.g., near a grip area of the steering wheel 410), heartratesensors respiration sensors, cameras, and/or the like.

In some cases, the input device 450 may be installed by a vehiclemanufacturer and be permanently affixed to a surface of the vehicleinterior. In other cases, the input device 450 may be installed as anafter-market device. In either case, the input device may becommunicatively coupled to a communication device (not shown) that maybe configured to communicate the subjective risk information to one ofthe user device 450, such as by using Bluetooth, or other local wirelessor wired communication protocol. In other cases, the input device may beconfigured to communicate subjective risk information via a cellularnetwork directly to the subjective risk analysis system 250. In somecases, one or more sensors may be used (e.g., biometric sensors,pressure sensors, microphones, etc.) may be used to generate a signalrepresentative of a driver's feeling of unease, without the driverconsciously providing the information. For example, one or morebiometric sensors may be used to sense an increase in a heart rate,breathing rate, and/or the like. In other cases, a pressure sensor maybe embedded within the steering wheel of the vehicle and configured forsensing a grip pressure (e.g., an ongoing pressure). Such examplesillustrative and are not to limit the sensor type or location to theenumerated examples.

In an illustrative example, the input device 450 may include one or morebiometric sensors capable of sensing biometric information correspondingto the driver. The input device 450 may include a sensor for measuring arate of a heartbeat, such as by using an infrared sensor. For example,an infrared (e.g., heat) sensor may be used to measure a heartbeat ratethrough the skin (e.g. of the finger) of the driver. In some cases, aheartbeat, and/or the heartbeat rate (relative to a baseline measuredfor the driver) may be used to determine when a user is experiencing asubjective risk event as well as the relative level of subjective riskbeing experienced. For example, the subjective risk analysis computingsystem may analyze the sensor data and relative heartbeat rate whendetermining the subjective risk score for each of a plurality of roadsegments along the route. In an illustrative example, the input device450 may further comprise a biometric sensor measuring an eye movement,such as by using an imaging device such as a video camera, and/or a heat(infrared) sensor, etc. For example, a video camera may be locatedwithin the interior space for the vehicle 400. This video camera may beused to monitor eye activity (e.g., a movement, etc.) to determinewhether a driver's eyes are relaxing and/or whether the driver isfalling asleep. In another illustrative example, the video camera maycapture whether a driver is intensely concentrating or looking rapidlyaround in many different directions to determine when a user isexperiencing a subjective risk event as well as the relative level ofsubjective risk.

In an illustrative example, one or more pressure sensors may be includedin the steering wheel to monitor an on-going pressure on the wheel. Forexample, a time duration corresponding to a light grip pressure may becorrelated to one or more route segments in which the driver has minimalstress. Similarly, a time duration corresponding to a higher grippressure may be correlated to one or more route segments in which thedriver has experienced an elevated level of stress. A short duration ofhigher pressure may correspond to a location along a route in which adriving event occurred. In such a manner, a driver's stress level may bemeasured without, or with minimal, input on the driver's part.Similarly, an imaging device, such as a video camera may be positionedso that the driver's eye activity may be monitored to determine whetherthe driver's eyes are relaxing, if the driver is falling asleep, if thedriver is intensely concentrating or looking rapidly around in manydifferent directions. Such eye activity may be correlated to levels oflow stress (e.g., relaxing eyes, falling asleep) or levels of elevatedstress (e.g., intense concentration, looking rapidly, etc.). The sensorsmay be used instead of, or in combination, with other methods ofmeasuring a level of driver unease (e.g., stress). For example, one ormore thermal sensors or thermal imaging sensors (e.g., an infraredsensor) may be used to monitor a driver's heartbeat, where an increasingheart rate may be correlated to an increasing level of stress. Also, ifa driver's temperature is increasing (e.g., heating up) may becorrelated to an increasing level of frustration, anger, or other suchemotion.

In an illustrative example, the driver of the vehicle may experience oneor more instances of unease at particular times and/or places along aroute. In doing so, the driver may use the input device 450, 445 toindicate that a subjective risk event has occurred. In doing so, thedriver may generate subjective risk information that may be used todetermine the driver's tolerance level towards one or more types ofsubjective risk (e.g., heavy traffic, poor weather conditions, etc.).When triggered, the input device 445, 447 may cause another device, suchas the user device 440 to capture and/or store subjective riskinformation corresponding to the event.

Likewise, the user device 440 may also receive other information toenhance the accuracy of the risk value associated with a travel route.For example, the user device 440 may receive the time of day when thedriver is driving (or plans to drive) through a particular travel route.This information may improve the accuracy of the risk value retrieved(in for the travel route. For example, the driver may know, or suspect,that a particular segment of road through a wilderness area may have ahigher rate of accidents involving deer during the night hours. As such,the driver may experience unease when driving along that route segmentand may utilize the input 447, 450 to indicate this feeling on unease.In some cases, this route segment through the wilderness area may have ahigher rate of accidents involving wildlife (e.g., deer, etc.) duringthe night hours, than during daylight hours. In such cases, the drivermay not indicate any feeling of unease when travelling this routesegment during the day. However, the driver may still experience stressalong that particular stretch of road during the night hours. Therefore,the time of day may also be considered when determining the appropriatesubjective risk value. In addition, the user device 440 may receiveother information to improve the accuracy of the risk value retrievedfor a travel route. Some examples of this other information include, butare not limited to, the vehicle's speed (e.g., a vehicle without a sportsuspension attempting to take a dangerous curve at a high speed),vehicle's speed compared to the posted speed limit, etc. In some cases,the user device 440 may include a location device (e.g., a globalpositioning device, a cellular network positioning system, etc.) todetermine a location associated with the perceived feelings of unease bythe driver.

In some cases, such as when the user device 450 is a device capable ofcommunication via a cellular network, the subjective risk events may becommunicated from the vehicle 210 to the subjective risk analysis system250 via the cellular network in near real-time. In other cases, thesubjective risk event information may be stored within a memory of theuser device 440 until the user device 450 is able to communicate theinformation, such as when the user device is in proximity of a wirelessnetwork (e.g., a Wi-Fi network). The memory may be embodied in anon-volatile memory (e.g., in a memory in personal navigation device110) or portable media (e.g., CD-ROM, DVD-ROM, USB flash, etc. connectedto personal computing device 108).

In some cases, the vehicle computing system may be used in addition to,or in place of, one or more other components. In such cases, the vehiclecomputing system may be used to collect data, analyze data, calculateone or more weighting factors or otherwise customize the mathematicalalgorithm, and/or generate a subjective risk score for one or more routesegments.

FIG. 5 depicts an illustrative method 500 for determining a subjectiverisk score associated with a driver of a vehicle 210 in accordance withaspects of this disclosure. At 510, the subjective risk analysis system250 may receive user profile information about a driver, such as fromthe account information database 254. The user account information mayinclude user demographic information, car make and model information forthe vehicle, driver's license information (e.g., personal driver'slicense, commercial driver's license, etc.), insurance policyinformation, a driver experience level, and the like. In some cases, theuser account information may include user group information. Forexample, a user group may correspond to a family group, a work group, afleet of vehicles, and the like. In an illustrative example, the familygroup may include information corresponding to a parent, a child and/orothers (e.g., a relative, a friend). Similarly, a work group or fleetgroup information may include a manager, a supervisor, and one or moredrivers. The user group information may allow a user with administrativepowers with regards to the user group set permissions and/or rulesassociated with the group. For example, one or both parents may havesupervisory powers over other members of the family group (e.g., achild, a friend, a relative, etc.) and may be able to generate rules(e.g., always choose a route minimizing subjective risks, etc.) and/orto specify one or more parameters (e.g., an experience level associatedwith a child). The manager and/or supervisor users of the commercial orfleet groups may have similar powers, where the manager and/orsupervisor users may be capable of setting rules and/or policiescorresponding to individual drivers. For example, the managers and/orsupervisors may be capable of setting an experience level and/or asubjective risk level for individual drivers within the fleet and/orcommercial groups. Such preference information may be stored within theaccount database 254

At 520, the risk analysis engine 252 of the risk analysis system 250 mayreceive supplemental subjective risk information corresponding to alevel of risk tolerance towards subjective risks that may be experiencedwhile driving the vehicle 210. For example, the subjective risk analysissystem 250 may communicate a form and/or a questionnaire to one or moreusers. The form and/or questionnaires may be communicated to a driver ofthe vehicle by mail, phone, email and/or the like. By filling out thequestionnaire, the user may confirm, modify, or replace one or moreparameters stored in the account information database associated withthe user. In some cases, the form and/or questionnaire may be presentedto the user via the one or more personal computing devices. For example,the user may be presented with a series of questions, such as thoselisted on a user interface screen of the one or more user devices 220.Once received the subjective risk analysis system may generate aninitial subjective risk score for the user based on the user profileinformation and the received supplemental information.

In some cases, the subjective risk score generated by the subjectiverisk computing system may be in the form of a number rating the risktolerance of the driver (e.g., a rating of 1 to 100 where 1 is very lowrisk tolerance and 100 is very high risk tolerance). Alternatively, thesubjective risk score may be in the form of a predetermined category(e.g., low risk tolerance, medium risk tolerance, and high risktolerance). At least one benefit of displaying the subjective risk scorein this form is the simplicity of the resulting display for the driverand/or the ease of user in using the subjective risk score incalculating a route minimizing potential subjective risks that thedriver may be subject to during the drive.

At 525, the subjective risk analysis system 250 may check to see whethersubjective risk information is available, where the subjective riskinformation correspond to one or more subjective risks experienced by adriver along a particular route. For example, a user device 220, 440 maysend a notification via the communication networks 205 that newsubjective risk information is available for analysis. Suchnotifications may occur at the end of a trip, at periodic time intervals(e.g., about 1 minute, about 5 minutes, about 1 hour, etc.), or in nearreal-time from when the user experienced the subjective risk event.

If so, at 530, the subjective risk analysis engine 252 may receive theinformation corresponding to one or more subjective risk events from theuser device 220, 440 associated with the driver. The subjective riskanalysis engine 252 may then generate a subjective risk score for theuser and/or the road segment using information in the user profileand/or the subjective risk information received from the user device220, 440.

In accordance with aspects of this disclosure, a subjective riskanalysis engine 252 may receive the user profile information, thesubjective risk information, geographic information, and/or vehicleinformation. The subjective risk analysis engine 252 may calculate therisk value for the user and/or the road segment (or point of risk) byapplying actuarial techniques to the information that may be receivedfrom the one or more third party information sources 280. In some cases,the user device 230, when possible, may receive and store subjectiverisk information in a data store with the latitude/longitude and time ofa subjective risk event. The subjective risk event data may beassociated with a location and combined with other subjective risk dataassociated with the same location. Applying actuarial and/or statisticalmodeling techniques involving multiple predictors, such as generalizedlinear models and non-linear models, a risk score may be calculated, andthe calculated risk value may be recorded in a memory device of thesubjective risk analysis engine. The multiple predictors involved in thestatistical model used to calculate the risk score may include accidentinformation, geographic information, and vehicle information.Associating the risk value with a line segment and/or point which bestpinpoints the area of the road in which the event(s) occurred may beaccomplished by using established GIS locating technology (e.g., GPSascertaining a geographically determinable address, and assigning thedata file to a segment's or intersection's formal address determined bythe system).

For example, two or more subjective risk events located in anintersection or road segment may have slightly different addressesdepending on where within the intersection or segment the subjectiverisk event location was determined to be. Therefore, the system mayidentify a location based on business rules. In another example businessrules may identify a subjective risk event location using the address ofthe nearest intersection. In yet another example the system may identifythe location of an incident on a highway using segments based on mileagemarkers or the lengths may be dynamically determined by creating segmentlengths based on relatively equal normalized risk values. Therefore,roadways that have stretches with higher numbers of accidents may haveshorter segments than stretches that have fewer accidents. In anotherexample, if the incident occurred in a parking lot, the entire parkinglot may be associated with a formal address that includes all accidentslocated within a determined area. One skilled in the art will appreciateafter review of the entirety disclosed that road segment includes asegment of road, a point on a road, and other designations of a location(e.g., an entire parking lot). At 540, the subjective risk analysissystem 250 may generate the subjective risk score and/or generate asubjective risk classification corresponding to the user and/or the roadsegment.

Returning to 525, if no subjective risk information is available for oneor more route segments associated with the user, the subjective riskanalysis engine 252 may generate the subjective risk score and/or asubjective risk classification for the user based on the retrievedaccount information and the supplemental information provided by theuser at step 520.

In some cases, the subjective risk analysis engine 252 may be configuredto automatically update the subjective risk and/or the subjective riskclassification associated with the user and/or the subjective risk scoreassociated with a particular road segment. For example, the subjectiverisk scores may be updated periodically and/or updated when newsubjective risk information is received by the subjective risk analysisengine.

At 545, the subjective risk analysis engine 252 may monitor a timerand/or the communication interface to determine whether to check if newsubjective risk information is available. For example, upon expirationof a periodic timer, the subjective risk analysis engine 252 may querythe user device whether new information is available at 525. In othercases, the subjective risk analysis engine 252 may monitor thecommunication interface for an indication (e.g., a flag) that newsubjective risk information has been received. If no update is to bedone, the system may wait for a period of time (e.g., an expiration of atimer) or for an event, such as a received message over thecommunication networks 205.

FIG. 6 depicts an illustrative method 600 for processing subjective riskinformation provided by a user while traveling upon a road segment inaccordance with aspects of this disclosure. At 610, a user may determinewhich of several possible routes provided by the subjective riskanalysis engine 252 to use while traveling within a vehicle. In somecases, the routes may be stored within a memory on a personal computingdevice, such as the smart phone 220 a, and/or the personal navigationdevice 110 b. Once selected, the user may proceed along the route. If noinput was received at 625, the user device 440 will continue to wait forthe user input at 620.

When a user input has been received at 625, the user device 440 maystore an indication of the subjective risk event in a memory device,with or without additional information regarding the event. In somecases, the user device may append a time of the subjective risk eventand/or a location associated with the subjective risk event triggered bythe user via the input device 445, 450 and/or the microphone 457. Insome cases, further additional information may be appended, such asgeographical information obtained from a positioning system within theuser device 440 or in communication with the user device 440. In somecases, the input device 445, 450 and/or the microphone 457 may provide,not only a binary indication of whether a subjective risk even hasoccurred, but information regarding a severity and/or duration of thesubjective risk event. For example, during a subjective risk event, theinput device 450 installed in the vehicle may comprise a button. Thisbutton may be monitored to determine a period of time during which thebutton was depressed. This length of time may be indicative of a timeduration during which the subjective risk was experienced by a user. Forexample, the user may experience a period of unease while driving alonga road segment in thick fog. In other cases, a severity of thesubjective risk event may be determined by using pressure informationregarding how hard the driver pressed the input device 450. For examplea greater pressure may be indicative of a higher feeling of unease and alesser pressure may be indicative of a lesser feeling of unease felt bythe driver. In some cases, the severity of the event may change over theduration of time associated with the subjective risk event. For example,the user may feel a greater sense of unease at a start of an event thanat the conclusion, or vice versa.

At 635, the user device 450 may determine a communication method to beused when communicating the subjective risk information to thesubjective risk analysis engine 252 at the remote computing system 240.For example, if near real-time communication is possible (e.g., the userdevice 440 has access to a cellular communication network), the userdevice may communicate the subjective risk information when, or closeto, the time at which the subjective risk event occurs at 640. If,however, near real time communication is not possible at 635, the userdevice 440 may be configured to store the subjective risk informationlocally to the device until such a time that communication via thenetworks 205 is possible, such as when the user device is able to accessa Wi-Fi network. After either communicating the subjective riskinformation in near real time or storing the subjective risk informationlocally to the user device 440, the user device 440 may wait for anotheruser input to be received at 620.

In some cases, the subjective risk information may be aggregated over aplurality of users to determine a total subjective risk associated witha plurality of routes indicated upon the subjective risk map overlay.This overlay may be used with an objective risk map to determine a totalrisk associated with each of one or more route segments indicated on themap. These graded route segments may be used to generate routes fordrivers based upon a risk classification. This risk classification maybe associated with the particular driver's risk score corresponding to atolerance to subjective risks that may be encountered. In other cases,the subjective risk tolerance information associated with the users maybe used to generate a route based upon groupings of individuals, such asby age (e.g., drivers under age 25, drivers over age 65, drivers betweenthe ages of 25-65, etc.), tolerance to risk, and the like. A total routesubjective risk value may be divided by the distance traveled todetermine the route subjective risk category for the travel route. Forexample, a route subjective risk category may be assigned based on a setof route subjective risk value ranges for low, medium, and high riskroutes.

After being aggregated, the total subjective risk value may be sent to aviewable display on the personal navigation device 110 b or user device440. Alternatively, the total subjective risk value may be sent to alocal/remote memory where it may be recorded and/or monitored. Forexample, it may be desirable for a safe driver to have her total riskvalue for all travel routes traveled over a time period to be uploadedto an insurance company's data store to be analyzed or otherwiseprocessed by a computer system at the insurance company, such as theinsurance quotation system 290. The insurance company may then identifythe driver as a lower-risk driver (e.g., a driver that travels onstatistically lower-risk routes during lower-risk times) and provide thedriver/vehicle with a discount and/or credit on an existing insurancepolicy (or towards a future insurance policy). At least one benefit ofthe aforementioned is that safe drivers are rewarded appropriately,while high-risk drivers are treated accordingly.

In some embodiments in accordance with aspects of this disclosure, theroute subjective risk value sent may be in the form of a number ratingthe subjective risk of the travel route (e.g., a rating of 1 to 100where 1 is very low subjective risk and 100 is very high subjectiverisk). Alternatively, the route subjective risk value may be in the formof a predetermined category (e.g., low subjective risk, mediumsubjective risk, and high subjective risk). At least one benefit ofdisplaying the route subjective risk value in this form is thesimplicity of the resulting display for the driver. For example, anenhanced GPS unit may display a route (or segment of a route) in a redcolor to designate a high subjective risk route, and a route may bedisplayed in a green color to designate a lower subjective risk route.At least one benefit of a predetermined category for the routesubjective risk value is that it may be used as the means for comparingthe amount of subjective risk associated with each travel route whenproviding alternate routes. In addition, the enhanced GPS unit may alertthe driver of a high subjective risk road segment and offer the driveran alternate route to avoid that segment.

In some cases, subjective risk values may be used when predicting alikelihood of a driver to experience an accident. For example, theuser's subjective risk may be a contributing factor in an accident. Forexample, a driver may experience a high level of subjective risk whendriving in fast moving, but heavy traffic. In such cases, this drivermay slow down and an accident may occur with a driver moving faster andnot paying attention. Additionally, a driver with a low subjective riskvalue may be too comfortable in driving during difficult conditions. Insuch cases, the driver may drive too fast for conditions, thus causingan accident.

When retrieving subjective risk values, in accordance with aspects ofthis disclosure, one or more techniques, either alone or in combination,may be used for identifying and calculating an appropriate overall riskvalue for road segments, where the subjective risk values may be used toprovide customizable weighting factors for each driver and for use ingenerating an overall risk value. For example, under an accident costseverity rating (ACSR) approach, each point of an overall risk has avalue which measures how severe the average accident is for each pointof risk. The value may be normalized and/or scaled by adjusting therange of the values. For example, under an ACSR approach using a rangeof values from 1 to 10: considering all accidents that occur in apredetermined area (e.g., road segment, state, zip code, municipality,etc.), the accidents in the top ten percentile of expensive accidents inthat territory would get a 10 value and the lowest 10 percentile ofcostly accidents in that region would get a 1 value. The actual losscost may be calculated by summing the various itemized loss costs (e.g.,bodily injury, property damage, medical/personal injury protection,collision, comprehensive, uninsured/underinsured motorist, rentalreimbursement, towing, etc.). In some cases, a driver's subjective riskprofile may be used to identify modifiers and/or weighting factors foruse in determining a contribution for one or more itemized loss costs.For example, a subjective risk score may be used to predict one or moreroad segments in which a driver may have an increased awareness so thatan itemized loss cost (e.g., a collision cost, etc.) may be reduced.

In an alternate embodiment, the ACSR approach may attribute varyingweights to the different types of loss costs summed to calculate theactual loss cost. For example, after analyzing the information, certainportions of a loss cost (e.g., medical cost) may indicate risk moreaccurately than others. The importance of these portions may be weightedmore heavily in the final loss cost calculation. Actuarial methods maybe used to adjust loss cost data for a segment where a fluke accidentmay cause the calculated risk value to far exceed the risk value basedon all the other data. In some cases, a driver's subjective risk profilemay be used to adjust the weights, either up or down based on apredicted subjective risk score for a particular road segment.

Under the accidents per year (APYR) approach, in accordance with aspectsof this disclosure, each point of risk has an overall risk value thatmay reflect the average number of accidents a year for that individualpoint of overall risk. Under a modified APYR approach, the overall riskvalue for a point of overall risk continues to reflect the averagenumber of accidents a year, but attributes a lesser weight to accidentsthat occurred a longer time ago, similar to time relevancy validation(e.g., it gives emphasis to recent accident occurrences over olderoccurrences). Similarly, the newer subjective risk scores in asubjective risk profile may contribute more to a calculation of theweights used in the APYR approach, than the older subjective risk scoresin recognition that driver's perceptions change over time with greaterexperience.

Under the risk severity (RSR) approach, in accordance with aspects ofthis disclosure, each point of risk has a risk value that may reflectthe severity of objective risk for that individual point of risk. Forexample, an intersection that is a frequent site of vehicle accidentrelated deaths may warrant a very high risk value under the RSRapproach. In one embodiment, risk severity rating may be based onaccident frequency at intersections or in segments over a determinedperiod of time. In another embodiment, the rating may be based on losscosts associated to intersections and segments. Yet another embodimentmay combine accident frequency and severity to form a rating for asegment or intersection. One skilled in the art can recognize that riskseverity ratings may be based on one or a combination of factorsassociated with intersections or segments. In some cases, the subjectiverisk score for a driver may be used to adjust the weights used in theRSR approach, based on an understanding that a driver having asubjective risk score within a particular range (e.g., about 30 to about75, etc.) may be more cautious and/or alert in a particular situation orroute segment. A driver having too low a subjective risk score (e.g.,less than about 10) may not be alert enough and a driver having too higha subjective risk score (e.g., greater than about 85, etc.) may be toocautious and may not react quickly enough or may overreact to thedriving conditions present at the route segment.

Under the Environmental Risk Variable (ERV) approach, in accordance withaspects of this disclosure, each point of risk has a risk value that mayreflect any or all information that is not derived from recordedaccidents and/or claims, but that may be the (direct or indirect) causeof an accident. In one embodiment, the risk value under the ERV approachmay be derived from vehicle information transmitted by a data source104, 106. In an alternate embodiment, the EVR approach may use compoundvariables based on the presence or absence of multiple subjective riskconsiderations which are known to frequently, or severely, causeaccidents. A compound variable is one that accounts for the interactionsof multiple risk considerations, whether environmental or derived fromrecorded accidents and/or claims. For example, driving through awildlife crossing zone at dusk would generate a greater subjective riskvalue and/or objective risk value than driving through this same area atnoon. The interaction of time of day and geographic area and/orgeographic type would be the compound variable. Another example mayconsider current weather conditions, time of day, day of the year, andtopography of the road. A compound variable may be the type ofinfrequent situation which warrants presenting a verbal warning to adriver (e.g., using a speaker system in a personal navigation device 110mounted in a vehicle) of a high objective risk route (e.g., a highobjective risk road segments).

Another possible approach may be to calculate the route subjective riskvalue using one or more of the approaches described above divided by thelength of the route traveled. This may provide an average routesubjective risk value for use in conjunction with a mileage rating plan.In one embodiment, the system combines route subjective risk andconventional mileage data to calculate risk per mile rating.

In one embodiment, a device in a vehicle (e.g., personal navigationdevice 110, mobile device 112, etc.) may record and locally store theroute and/or the route and time during which a route was traveled. Thistravel route information may be uploaded via wireless/wired means (e.g.,cell phones, manually using a computer port, etc.). This travel routeinformation may be used to automatically query a data source 104, 106for route rating information and calculate a total risk value.

The subjective risk types described above may be variables in amultivariate model of insurance losses, frequencies, severities, and/orpure premiums. Interactions of the variables would also be considered.The coefficient the model produces for each variable (along with thecoefficient for any interaction terms) would be the value to apply toeach subjective risk type. The user device 440 and/or the personalnavigation device 110 may initially provide the quickest/shortest routefrom a start location A to an end location B, and then determine theroute subjective risk value by determining either the sum product of thenumber of each subjective risk type and the value for that subjectiverisk type or the overall product of the number of each subjective risktype and the value for that subjective risk type. (Traffic and weatherconditions could either be included or excluded from the determinationof the route subjective risk value for comparison of routes. If notincluded, an adjustment may be made to the route risk value once theroute has been traveled). The driver may be presented with an alternateroute which is less risky than the initial route calculated. The userdevice 440 and/or the personal navigation device 110 b may display thedifference in subjective risk between the alternate routes and permitthe driver to select the preferred route. In some embodiments inaccordance with this disclosure, a driver/vehicle may be provided amonetary benefit (e.g., a credit towards a future insurance policy) forselecting a route having less subjective risk.

In another embodiment: the insurance policy is sold and priced in partbased on where a customer falls within a three sigma distribution ofrisk units consumed by all insured per a typical policy period. Thepolicy pricing may be based on an initial assumption of risk to beconsumed in the prospective policy period or may be based on subjectiverisk consumed in a preceding policy period. In a case where the numberof risk units consumed is greater than estimated, the customer may bebilled for the overage at the end of (or during) the policy period. Inyet another embodiment, the system may be provided as a pay-as-you-drivecoverage where the customer is charged in part based on the actual riskunits consumed in the billing cycle. The system may include a telematicsdevice that monitors, records, and periodically transmits theconsumption of risk units to processor 114 that may automatically billor deduct the cost from an account.

While this disclosure has been described with respect to specificexamples including presently exemplary modes of carrying out thisdisclosure, those skilled in the art will appreciate that there arenumerous variations and permutations of the above-described systems andtechniques that fall within the spirit and scope of this disclosure.

What is claimed is:
 1. A route analysis computer system, comprising: atleast one processor; and memory storing instructions that, when executedby the at least one processor, cause the route analysis computer systemto: receive, from a computing device associated with a user travellingwithin a vehicle, a biometric signal representative of a level of uneaseor alertness being experienced by the user during a subjective riskevent on a road segment; determine, based on biometric signal, a levelof subjective risk for the road segment; update risk information basedon the level of subjective risk for the road segment; generate, based onthe risk information, a route comprising one or more road segmentshaving a predetermined level of subjective risk along the route;display, via a vehicle display device, indication of levels ofsubjective risk of the one or more road segments of the route; andoverlay, on a digital map, the indication of the levels of subjectiverisk of the one or more road segments of the route over geographicinformation associated with the one or more road segments of the route.2. The route analysis computer system of claim 1, wherein the one ormore road segments of the route alternate between periods of a higherlevel of subjective risk and periods of a lower level of subjectiverisk.
 3. The route analysis computer system of claim 1, wherein theroute is associated with a level of subjective risk that is higher thana minimized level of subjective risk.
 4. The route analysis computersystem of claim 1, wherein the receiving the biometric signal comprisesreceiving sensor data produced by a biometric sensor.
 5. The routeanalysis computer system of claim 4, wherein the biometric sensorcomprises one or more a blood pressure sensor or a heartrate sensor. 6.The route analysis computer system of claim 4, wherein the biometricsensor comprises one or more a video camera or an infrared sensor. 7.The route analysis computer system of claim 1, wherein the indicationcomprises one or more of a first color associated with a high level ofsubjective risk or a second color associated with a low level ofsubjective risk.
 8. A method, comprising: receiving, by a route analysiscomputer system and from a computing device associated with a usertravelling within a vehicle, a biometric signal representative of alevel of unease or alertness being experienced by the user during asubjective risk event on a road segment; determining, based on biometricsignal, a level of subjective risk for the road segment; updating riskinformation based on the level of subjective risk for the road segment;generating, based on the risk information, a route comprising one ormore road segments having a predetermined level of subjective risk alongthe route; displaying, via a vehicle display device, indication oflevels of subjective risk of the one or more road segments of the route;and overlaying, on a digital map, the indication of the levels ofsubjective risk of the one or more road segments of the route overgeographic information associated with the one or more road segments ofthe route.
 9. The method of claim 8, wherein the one or more roadsegments of the route alternate between periods of a higher level ofsubjective risk and periods of a lower level of subjective risk.
 10. Themethod of claim 8, wherein the route is associated with a level ofsubjective risk that is higher than a minimized level of subjectiverisk.
 11. The method of claim 8, wherein the receiving the biometricsignal comprises receiving sensor data produced by a biometric sensor.12. The method of claim 11, wherein the biometric sensor comprises oneor more a blood pressure sensor or a heartrate sensor.
 13. The method ofclaim 11, wherein the biometric sensor comprises one or more a videocamera or an infrared sensor.
 14. The method of claim 8, wherein theindication comprises one or more of a first color associated with a highlevel of subjective risk or a second color associated with a low levelof subjective risk.
 15. One or more non-transitory computer-readablemedia storing instructions that, when executed by a route analysiscomputer system comprising at least one processor and memory, cause theroute analysis computer system to: receive, from a computing deviceassociated with a user travelling within a vehicle, a biometric signalrepresentative of a level of unease or alertness being experienced bythe user during a subjective risk event on a road segment; determine,based on biometric signal, a level of subjective risk for the roadsegment; update risk information based on the level of subjective riskfor the road segment; generate, based on the risk information, a routecomprising one or more road segments having a predetermined level ofsubjective risk along the route; display, via a vehicle display device,indication of levels of subjective risk of the one or more road segmentsof the route; and overlay, on a digital map, the indication of thelevels of subjective risk of the one or more road segments of the routeover geographic information associated with the one or more roadsegments of the route.
 16. The one or more non-transitorycomputer-readable media of claim 15, wherein the one or more roadsegments of the route alternate between periods of a higher level ofsubjective risk and periods of a lower level of subjective risk.
 17. Theone or more non-transitory computer-readable media of claim 15, whereinthe route is associated with a level of subjective risk that is higherthan a minimized level of subjective risk.
 18. The one or morenon-transitory computer-readable media of claim 15, wherein thereceiving the biometric signal comprises receiving sensor data producedby a biometric sensor.
 19. The one or more non-transitorycomputer-readable media of claim 18, wherein the biometric sensorcomprises one or more a blood pressure sensor or a heartrate sensor. 20.The one or more non-transitory computer-readable media of claim 18,wherein the biometric sensor comprises one or more a video camera or aninfrared sensor.